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Enterprises employ many types of data classification to organize and categorize their data based on its content, sensitivity, and importance. This process helps in managing and securing data more effectively by assigning labels or tags that indicate the level of confidentiality, integrity, and availability associated with the information.
While some types of data classification might be better suited for certain use cases or applications, every type of data classification shares the common goal of making sure your data is handled appropriately, stored securely, and accessed only by authorized individuals. We’ve detailed seven of the most common data classification types to help you understand their differences and how you might apply them to your own data needs.
Table of Contents
Public Data
Public data refers to information deliberately made accessible to everyone. This data is unrestricted in terms of access, posing no substantial risks even if it were to be disclosed. This data classification type is usually meant for widespread distribution, such as announcements on a public board.
Examples of public data:
- Government agency publications like official reports or census data
- Educational resources such as research papers, lecture notes, and publicly shared courses
- Research studies, articles, and findings from open access journals
Organizations that handle public data:
- Government agencies
- Nonprofit organizations
- Research institutions
- Media outlets
Internal Data
Internal data is a type of data classification for information intended for exclusive use within your organization. It may include sensitive information or non-sensitive data not meant for public disclosure. Internal data contains details related to your business operations, processes, and assets. It is protected and restricted to authorized personnel within your organization.
You can think of internal data like information on your personal computer or phone. You might share it selectively, but it’s not freely available to everyone.
Examples of internal data:
- Employee information such as personal records, payroll, performance evaluations, and employee IDs
- Confidential financial information, budgetary data, financial statements, transaction records
- Information related to patents, trademarks, trade secrets, and other forms of intellectual property
- Business plans, marketing strategies, competitive analyses
- Information about internal processes, supply chain information, manufacturing processes
Organizations that handle internal data:
- Both small and large companies
- National, regional, local government organizations
- Healthcare institutions like hospitals and clinics
- Banks, credit unions, and financial service providers
- Research and development institutions
Confidential Data
A subset of internal data, confidential data includes highly-sensitive information that needs stringent security measures. This data classification type often comes with strict access controls to prevent unauthorized disclosure or access. Confidential data may include information that, if exposed, could have significant adverse effects on your organization’s operations, reputation, or security.
Essentially, this data can be likened to information under lock and key. The goal is to protect it securely, maintain its integrity, and avoid any misuse.
Examples of confidential data:
- Trade secrets or information that gives a competitive edge; proprietary formulas, manufacturing processes, business strategies
- Detailed financial projections, mergers and acquisitions plans, undisclosed financial agreements
- Personal identifiable information (PII) like Social Security numbers, credit card details, or personal health information
- Confidential legal agreements, contracts, legal advice
- Access credentials, encryption keys, security-related information
- Details related to internal investigations, such as employee misconduct
- Sensitive research data not yet ready for public disclosure
Organizations that handle confidential data:
- Government intelligence agencies
- Banks, investment firms, and financial organizations
- Healthcare organizations like hospitals, pharmaceutical companies, and medical research institutions.
- Companies developing cutting-edge technologies
- Law firms
Restricted Data
Restricted data is information that carries additional limitations on access beyond what is considered confidential, often due to legal regulations, contractual obligations, or heightened sensitivity. Like some data classification types, it requires stricter controls and may be subject to specific compliance requirements.
Access to this data type is similar to having a VIP section at an event—not everyone gets in; only those with the appropriate credentials or clearance can enter.
Examples of restricted data:
- Comprehensive financial statements, budget information, payroll records
- Sensitive information related to intellectual property, such as patents, trade secrets, or proprietary algorithms
- Classified information, including national security details, intelligence reports, or military strategies
- Patient health records containing detailed medical history, treatment plans, or sensitive health information
- Legal agreements, contracts, privileged attorney-client communications
- Data collected in research studies involving human subjects
- Employee information, performance evaluations, disciplinary actions
- Business contracts, trade agreements, negotiations
Organizations that handle restricted data:
- National security agencies
- Healthcare Institutions
- Financial Institutions and banks
- Legal Firms
- R&D organizations building patented technologies
Private Data
While other types of classification of data are generally used by organizations, private data belongs to an individual. This is information that is highly personal and not meant for public consumption.
Private data can be compared to your personal journal. Access to this data is tightly controlled, and it’s intended for your eyes only.
Examples of private data:
- Personal identifiers, such as Information such as names, social security numbers, passport numbers, and driver’s license numbers
- Contact information
- Personal financial information, bank account numbers, credit card details, financial transactions
- Medical records, prescription details, and other health-related data
- Fingerprints, retinal scans, and other biometric identifiers
- Details about individual preferences, habits, and lifestyle choices
Organizations that handle private data:
- Hospitals, clinics, and healthcare providers
- Financial institutions
- Online retailers
- Telecommunication companies
- HR departments
Critical Data
Critical data is indispensable for an organization’s operation and survival. Unauthorized access to or loss of this information could result in severe consequences. Exposure of this data type can potentially impact the functionality or reputation of your enterprise.
Critical data serves as the foundation of a company, much like the foundation of a building. If this foundation is compromised, the entire structure is at risk of collapse.
Examples of critical data:
- Network infrastructure and system configurations essential for operations
- Emergency response plans
- Customer databases
- Business continuity plans
Organizations that handle critical data:
- Energy and utility companies
- Banks and financial organizations
- Hospitals and healthcare providers
- National and local government agencies
- Technology companies
Regulatory Data
This type of data classification is subject to specific legal requirements or industry compliance standards, like General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), Sarbanes-Oxley Act (SOX), or other relevant regulations. These regulations dictate how data should be collected, processed, stored, and shared. Adherence to these standards is imperative to avoid legal consequences and ensure responsible data management.
Regulatory data is like a set of rules and guidelines that everyone in a community follows, such as traffic laws. It is necessary to follow these guidelines to maintain order within a particular environment.
Examples of regulatory data:
- Information covered by data protection laws
- Data related to financial transactions and customer financial information
- Patient health records, medical information, and other healthcare-related data
- Environmental and workplace safety data
- Information related to telecommunications services, including customer records and communication logs
Organizations that handle regulatory data:
- Banks, investment firms, and other financial organizations.
- Hospitals, medical institutions, and healthcare providers
- Technology and e-commerce companies
- Telecom providers
- Energy and utility companies
FAQ
Why Should Data be Classified?
Data should be classified as it is a data management best practice that bolsters the protection of data assets. Data classification enables the prioritization of critical data, targeted risk assessments, and the application of suitable security measures. Moreover, it ensures compliance with data protection laws, aids in creating data retention policies, and extends security protocols to the supply chain.
How to Classify Data
For effective data classification the following key steps are important:
- Catalog various data types and their locations.
- Set sensitivity levels like “Public,” “Internal,” “Confidential,” and “Restricted.”
- Formulate clear policies outlining criteria and security measures.
- Use tools for automated data identification and classification.
- Implement controls to restrict unauthorized access based on data classification.
- Protect sensitive data using encryption.
- Track access and check compliance through monitoring and auditing.
- Keep data classification policies up-to-date by reviewing and updating them periodically.
What are Data Classification Tools?
Data classification tools are various solutions that maintain data security and compliance with regulations. These tools safeguard sensitive information and intellectual property within your organization. Some types of data classification tools include:
- Data loss prevention (DLP) software: Protects sensitive data from unauthorized access, disclosure, or leakage. It monitors, detects, and prevents the illegal transmission of sensitive data.
- Metadata tagging tools: Add metadata tags to files or documents to indicate sensitivity levels and types of data classifications.
- Content discovery and classification tools: Scan and analyze content to identify and classify sensitive information using pattern matching and keyword analysis.
- Encryption solutions: Safeguard sensitive data by applying strong encryption strategies. These advanced tools accurately classify data and streamline the implementation of appropriate encryption methods for optimal security.
Bottom Line
Data classification is the foundation of solid data management that supports all other data management activities. By properly classifying data, your organization can have confidence that each piece of information is handled correctly, enhancing security, compliance, and overall data governance.
Proper data classification can protect enterprise assets and guide you to stay on the right side of regulations. There are seven data classification types—public, internal, confidential, restricted, private, critical, and regulatory. Each is a nod to the many facets of data and the different levels of protection they need.
It is important to understand that each classification calls for a unique strategy. This involves implementing robust policies and employing appropriate tools to keep data safe. It’s a bit of a balancing act between making data accessible and keeping it secure. Ultimately, a well-defined data classification strategy serves as a formidable defense against potential breaches. It fosters trust, guarantees regulatory compliance, and facilitates effective data management.
Proper data classification is just one of the many data management best practices. It’s a crucial step, but it’s part of a larger picture. Read our comprehensive guide on data management best practices to take your data management strategies to the next level.
